Data Structures and Algorithms in Java
Data Structures and Algorithms in Java
6th Edition
ISBN: 9781118771334
Author: Michael T. Goodrich
Publisher: WILEY
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Chapter 4, Problem 54C

Explanation of Solution

a.

Simple method to compute p(x):

Let us consider the “p(x)” polynomial of degree “n”.

The method to compute the polynomial of degree is given below:

//Define the simplePolynomial() method

public static double simplePolynomial(double a[], double n)

{

  //Declare the variable

double sum = 0;

  //Loop executes until the length of array

for(int i=0; i<a.length; i++)

  //Compute the polynomial of degree

  sum = sum + a[i] * Math.pow(n, a.length - i - 1);

//Return the polynomial of degree

return sum;

}

Running time:

In the above code, the computation of polynomial of degree is given below:

  • For each and every array term,
    • a[0] is “0” operation.
    • a[1]× x is “1” operation.
    • a[2]× x × x is “2” operations.
    • a[3]× x × x × x is “3” operations

Explanation of Solution

b.

Method to compute p(x) in O(n(logn)) time:

Let us consider the “p(x)” polynomial of degree “n”.

The method to compute the polynomial of degree is given below:

//Define the Polynomial() method

public static double Polynomial(double a[], double n)

{

  //Declare the variable

double sum = 0;

  int i=0;

  //Loop executes until the length of array

while(i<a.length)

{

  //Compute the polynomial of degree

  sum = sum + a[i] * Math.pow(n, a.length - i - 1);

  //Increment "i" by "1"

  i = i++;

  }

//Return the polynomial of degree

return sum;

}

Running time:

In the above code, the computation of polynomial of degree is given below:

  • For each and every array term,
    • a[0] is “0” operation.
    • a[1]× x is “1” operation

Explanation of Solution

c.

Horner method to compute p(x):

Let us consider the “p(x)” polynomial of degree “n”.

The method to compute the polynomial of degree is given below:

//Define the HornerPolynomial() method

public static double HornerPolynomial(double a[],double n)

{

  //Declare the variable

double sum = 0;

  //Loop executes until the length of array

for(int i=1;i< a

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Students have asked these similar questions
Given an n-element array X of integers, Algorithm A executes an O(n) time computation for each even number in X and an O(log-n) time computation for each odd number in X. What are the best case and worst case for running time of algorithm C?
The order of growth of the function f(n)=2n×n is lower than the function g(n)= 2n×n2. Hence, f(n)=O(g(n)). Select one: a. None b. Yes c. No d. Maybe
The Fibonacci function f is usually defined as follows. f (0) = 0; f(1) = 1; for every n e N>1, f (n) = f(n – 1) + f(n – 2). Here we need to give both the values f(0) and f(1) in the first part of the definition, and for each larger n, f(n) is defined using both f(n - 1) and f(n- 2). Use induction to show that for every neN, f(n) 1; checking the case n = 1 separately is comparable to performing a second basis step.)

Chapter 4 Solutions

Data Structures and Algorithms in Java

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